Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/218220
Title: IMPROVING DEEP LEARNING-BASED FACADE VISUAL INSPECTION: A DATA QUALITY PERSPECTIVE
Authors: GUO JINGJING
ORCID iD:   orcid.org/0000-0003-2047-6703
Keywords: Facade visual inspection, Deep learning, Data quality problem, Condition evaluation, Total data quality management
Issue Date: 10-Dec-2021
Citation: GUO JINGJING (2021-12-10). IMPROVING DEEP LEARNING-BASED FACADE VISUAL INSPECTION: A DATA QUALITY PERSPECTIVE. ScholarBank@NUS Repository.
Abstract: The main objective of this thesis is to improve the performance of deep learning-based façade visual inspection. In this thesis, the performance is considered from the aspects of reliability and efficiency. To achieve this objective, this thesis designs a research methodology based on the theory of total data quality management. The methodology includes four phases: definition, assessment, analysis, and improvement. The research framework is unfolded through three procedures: data selection, data annotation, and model training. For each procedure, criteria are designed to assess the data quality, and solutions are developed enabling the target stage to focus on “better” data. The experiment results demonstrate that the proposed solutions improved the accuracy and stability of the façade defects detection. Besides, the detection results obtained by the proposed solutions provide more effective outcomes for condition evaluation. Meanwhile, the time and cost are saved in general perspective because no extra labor works are expended.
URI: https://scholarbank.nus.edu.sg/handle/10635/218220
Appears in Collections:Ph.D Theses (Open)

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